arXiv:1203.0529v2 [hep-ex] 10 May 2012
CERN-PH-EP-2012-032
Search for FCNC single top-quark production at
√
s = 7 TeV with the ATLAS detector
The ATLAS Collaboration
Abstract
A search for the production of single top-quarks via flavour-changing neutral-currents is presented. Data collected with the ATLAS detector at a centre-of-mass energy of √s = 7 TeV, corresponding to an integrated luminosity of 2.05 fb−1, are used. Candidate
events with a semileptonic top-quark decay signature are classified as signal or background-like events by using several kinematic variables as input to a neural network. No signal is observed in the neural network output distribution and a Bayesian upper limit is placed on the production cross-section. The observed upper limit at 95% confidence level on the cross-section multiplied by the t→ Wb branching fraction is measured to be σqg→t×B(t → Wb) < 3.9 pb. This upper limit is converted using a model-independent
approach into upper limits on the coupling strengths κugt/Λ < 6.9 · 10−3TeV−1and κcgt/Λ < 1.6 · 10−2TeV−1, where Λ is the new
physics scale, and on the branching fractionsB(t → ug) < 5.7 · 10−5andB(t → cg) < 2.7 · 10−4.
Keywords: top physics, heavy quark production, FCNC, single top quark
1. Introduction
The top quark is the heaviest elementary particle known, with a mass of mtop = 173.2± 0.9 GeV [1] that is close to the
elec-troweak symmetry breaking scale. For this reason it is an excel-lent object to test the Standard Model (SM) of particle physics. The properties of the top quark can be studied from proton-proton (pp) collisions at √s = 7 TeV with the Large Hadron Collider (LHC). Top-quark pair-production via the strong in-teraction has been measured at the LHC [2, 3], and its cross-section is in good agreement with the prediction of the SM. Additionally, top quarks can be singly produced through three different processes: t-channel, Wt associated production, and s-channel. Only t-channel single top-quark production has been observed so far [4–6]. According to the SM of particle physics, flavour-changing neutral-current (FCNC) processes are forbid-den at tree level and suppressed at higher orders due to the Glashow-Iliopoulos-Maiani mechanism [7]. Extensions of the SM with new sources of flavour predict higher rates for FC-NCs involving the top quark; these extensions include new ex-otic quarks [8], new scalars [9, 10], supersymmetry [11–14], or technicolour [15] (for a review see Ref. [16]). If the new parti-cles are heavy, which is consistent with the non-observation of low-mass new particles at the Tevatron and LHC, their effects on top-quark FCNCs can be parameterised in terms of a set of dimension-six gauge-invariant operators [17]. The predicted branching fractions for top quarks decaying to a quark and a photon, Z boson, or gluon can be as large as 10−5to 10−3 for
certain regions of the parameter space in the models mentioned. For heavy new particles these branching fractions can be large, if the new particles couple strongly to the SM particles.
According to the corresponding values of the unitary Cabibbo-Kobayashi-Maskawa matrix, the top quark decays al-most exclusively to a W boson and a b quark. FCNC
top-quark decays can be studied directly by searching for final states with the corresponding decay particles [18, 19]. However, the t → qg mode, where q denotes either an up quark u or a charm quark c, is almost impossible to separate from generic multijet-production via quantum chromodynamic (QCD) pro-cesses, and a much better sensitivity can be achieved in the search for anomalous single top-quark production. In the pro-cess studied here, a u or c quark and a gluon g coming from the colliding protons interact to produce a single top-quark. The most general effective LagrangianLefffor this process
re-sulting from dimension-six operators contains only tensor cou-plings [20] and it can be written as [21, 22]:
Leff= gs X q=u,c κqgt Λ ¯tσ µνTa( fL qPL+ fqRPR)qGaµν+ h.c., (1)
where the κugt, κcgtare dimensionless parameters that relate the
strength of the new coupling to the strong coupling constant gs. Λ is the new physics scale, related to the mass cutoff scale above which the effective theory breaks down. Taare the Gell-Mann matrices [23] and σµν = 2i[γµ, γν] transforms as a tensor under
the Lorentz group. The fqL,R are chiral parameters normalised
such that: | fqL|2+| fqR|2 = 1. The operator PL = 12(1− γ5)
per-forms a left-handed projection, while PR = 12(1 + γ5) performs
a right-handed projection, where γ5represents the chirality
op-erator. Ga
µνis the gauge-field tensor of the gluon and t and q are
the fermion fields of the top and light quark, respectively. The existence of FCNC operators allows not only the pro-duction of top quarks via qg→ t, but also the decays t → qg. In the allowed region of parameter space for κqgt/Λ an
experi-mentally favourable situation occurs when the FCNC produc-tion cross-secproduc-tion for single top-quarks is several picobarns, while the branching fraction for FCNC decays is very small, and top quarks can thus be reconstructed in the SM decay mode
t → Wb. The W boson can decay into quark-antiquark pairs (W → q1q¯2) or a lepton-neutrino pair (W → ℓν). In this
anal-ysis only the decay into a lepton-neutrino pair, the leptonic de-cay, is considered. Thus the complete process searched for is qg→ t → W(→ ℓν)b. Selected events are characterised by an isolated high-energy lepton (electron or muon), missing trans-verse momentum from the neutrino and exactly one jet, pro-duced by the hadronisation of the b quark. Events with a W boson decaying into a τ lepton, where the τ decays into an elec-tron or a muon are also selected. The process studied here can be differentiated from SM single top-quark production because the latter is usually accompanied by additional jets.
This analysis is the first search for FCNCs involving quarks and gluons at the LHC. A search for the 2→ 1 process qg → t was performed by CDF [24], while D0 set limits on κugt/Λ and κcgt/Λ by analysing the 2 → 2 processes q¯q → t¯u, ug → tg, and
gg→ t¯u and their c quark analogues [25].
2. Data sample and simulation
The ATLAS detector [26] is built from a set of cylindrical subdetectors, which cover almost the full solid angle1 around the interaction point.
ATLAS is composed of an inner tracking system close to the interaction point, surrounded by a superconducting solenoid providing a 2 T axial magnetic field, electromagnetic and hadronic calorimeters, and a muon spectrometer. The electro-magnetic calorimeter is a high-granularity liquid-argon (LAr) sampling calorimeter with lead absorber. An iron-scintillator tile calorimeter provides hadronic energy measurements in the central pseudorapidity range. The endcap and forward regions are instrumented with LAr calorimeters for both electromag-netic and hadronic energy measurements. The muon spectrom-eter consists of three large superconducting toroids, a system of trigger chambers, and precision tracking chambers.
This analysis is performed using √s = 7 TeV pp-collision data recorded by ATLAS between March 22 and August 22, 2011. Only the periods in which all the subdetectors were op-erational are considered, resulting in a data sample with a total integrated luminosity of 2.05± 0.08 fb−1[27, 28].
Detector and trigger simulations are performed with the stan-dard simulation of ATLAS within the GEANT4 [29, 30] frame-work. The same offline reconstruction methods used with data events are applied to the simulated samples. Minimum bias events generated by PYTHIA [31] are used to simulate multi-ple pp interactions, corresponding to the LHC operation with 50 ns bunch separation and an average of six additional pp in-teractions per bunch crossing.
For the simulation of FCNC production of single top-quarks, PROTOS [32] is used. The top quarks decay as expected in the 1In the right-handed ATLAS coordinate system, the pseudorapidity η is de-fined as η =− ln[tan(θ/2)], where the polar angle θ is measured with respect
to the LHC beamline. The azimuthal angle φ is measured with respect to the
x-axis, which points towards the centre of the LHC ring. The z-axis is parallel
to the anti-clockwise beam viewed from above. Transverse momentum and en-ergy are defined as pT= p sin θ and ET= E sin θ, respectively. The ∆R distance is defined as ∆R = p(∆η)2+ (∆φ)2.
SM, and only the leptonic decay of the W boson is considered. W bosons decaying into a τ lepton, where the τ decays into an electron or a muon are included in both the signal and all background samples. The CTEQ6 [33] leading-order (LO) par-ton distribution functions (PDFs) are used and the hadronisation of signal events is simulated with PYTHIA using the AMBT1 tunes [34] to the ATLAS collision data. It has been verified that the kinematics of the signal process are independent of the a priori unknown FCNC coupling.
Several SM processes are expected to have the same final-state topology as the signal. Samples of simulated events for the t-channel and Wt single top-quark processes are gener-ated by the AcerMC program [35] with the CTEQ6 LO PDFs and hadronised with PYTHIA; for the s-channel process, the MC@NLO [36] generator with the CTEQ6.6 [37] PDFs inter-faced to HERWIG [38] and JIMMY [39].
The ALPGEN [40] program with the CTEQ6 LO PDFs is interfaced to HERWIG and JIMMY to generate W+jets, Wb ¯b, Wc¯c, Wc and Z+jets events with up to five additional partons. To remove overlaps between the n and n + 1 parton samples the MLM matching scheme [40] is used. The double count-ing between the inclusive W + n parton samples and samples with associated heavy-quark pair-production is removed utilis-ing an overlap-removal method based on ∆R matchutilis-ing. The parameters of HERWIG, with the MRST LO** [41] PDFs, and JIMMY are tuned to ATLAS collision data with the correspond-ing AUET1 tunes [42]. Diboson backgrounds from WW, WZ and ZZ events are simulated using HERWIG. For the generation of SM t¯t events the MC@NLO generator with the CTEQ6.6 PDFs is used. The parton shower and the underlying event are added using HERWIG and JIMMY.
3. Event selection
Events are considered only if they were accepted by a single-lepton trigger [43]. The single-muon trigger threshold was pT = 18 GeV, and the single-electron trigger threshold was
raised from an ET of 20 GeV to 22 GeV for higher LHC
lu-minosities.
Electron candidates are defined as clusters of cells in the elec-tromagnetic calorimeter associated with a well-measured track fulfilling several quality requirements [44]. Electron candidates are required to satisfy pT > 25 GeV and |ηclus| < 2.47, where
ηclus is the pseudorapidity of the cluster of energy deposits in
the calorimeter. A veto is placed on candidates in the calorime-ter barrel-endcap transition region, 1.37 <|ηclus| < 1.52, where
there is limited calorimeter instrumentation. High-pT
elec-trons associated with the W-boson decay can be mimicked by hadronic jets reconstructed as electrons, electrons from de-cays of heavy quarks, and photon conversions. Since signal electrons from the W-boson decay are typically isolated from hadronic jet activity, these backgrounds can be suppressed via isolation criteria which require minimal calorimeter activity and only low track pT in an η-φ cone around the electron
candi-date. Calorimeter isolation requires the sum of the ET in cells
within a cone of ∆R = 0.3 around each electron with pT > 25
scalar sum of the pT of tracks around the electron must
sat-isfy P pT(∆R < 0.3)/pT < 0.15. The electron track pT and
the ET in associated cells are excluded fromP pT(∆R < 0.3)
and P ET(∆R < 0.3), respectively. Muon candidates are
re-constructed by matching track segments or complete tracks in the muon spectrometer with the inner detector tracks. The fi-nal candidates are required to have a transverse momentum pT> 25 GeV and to be in the pseudorapidity region of |η| < 2.5.
Isolation criteria are applied to reduce background events in which a high-pT muon is produced in the decay of a heavy
quark. For the transverse energy within a cone of ∆R = 0.3 about the muon direction, P ET(∆R < 0.3)/pT < 0.15 is
re-quired, while the scalar sum of transverse momenta of addi-tional tracks inside a ∆R = 0.3 cone around the muon must sat-isfyP pT(∆R < 0.3)/pT< 0.10. Candidate events are required
to have exactly one isolated lepton (ℓ).
Jets are reconstructed using the anti-kt algorithm [45] with
the distance parameter R set to 0.4. The jets are then corrected from the raw calorimeter response to the energies of the recon-structed particles using pT- and η-dependent factors, derived
from simulated events and validated with data [46]. Since the signal process gives rise to only one high-pT jet, exactly one
reconstructed jet with pT > 25 GeV is required.
The magnitude of the missing transverse momentum Emiss
T is defined as Emiss T = | ~E miss T |, where ~E miss
T is calculated using the
calibrated three-dimensional calorimeter energy clusters asso-ciated with the jet together with either the calibrated calorimeter energy cluster associated with an electron or the pTof a muon
track [47]. Transverse energy deposited in calorimeter cells but not associated with any high-pT object is also included in the
ETmisscalculation. Due to the presence of a neutrino in the final state of the signal process, EmissT > 25 GeV is required. To fur-ther reduce the number of multijet background events, which are characterised by low Emiss
T and low values of reconstructed
W-boson transverse mass mW
T = q 2hplepT Emiss T − ~p lep T · ~E miss T i , the event selection requires mW
T + E
miss
T > 60 GeV.
Finally, the selected jet has to be identified (b-tagged) as a b-quark jet. The tagging algorithm exploits the properties of a b-quark decay in a jet using neural-network techniques and the reconstruction of a secondary vertex, and has an identification efficiency measured to be about 57% in t¯t events [48]. Only 0.2% of light-quark jets and 10% of c-quark jets are mis-tagged as b-quark jets. The following samples are defined for this anal-ysis: a “b-tagged sample” with exactly one b-tagged jet, and a “pretagged sample” without any b-tagging requirement.
Assuming a cross-section of 1 pb for FCNC single top-quark production, about 113 signal events in 2.05 fb−1 of collision
data are expected in the b-tagged sample.
The normalisations for the various background processes are estimated either by using the experimental data or by using Monte Carlo simulation scaled to the theoretical cross-section predictions. For the W+jets and Z+jets backgrounds the kine-matic distributions are modelled using simulated events, while the inclusive cross-sections are calculated to next-to-next-to-leading order (NNLO) with FEWZ [49]. The dominant W+jets background process is Wc production, whose k-factor is
ob-tained by comparing the NLO and LO cross-sections calculated using MCFM [50]. The W+(1 jet) and Z+(1 jet) background normalisation uncertainties are estimated from the uncertainty in the cross-section of the W/Z+(0 jet) process and the uncer-tainty in the cross-section ratio of W/Z+(1 jet) to W/Z+(0 jet). A cross-section uncertainty of 4% is assigned for the W/Z+(0 jet) process. Variations consistent with experimental data are made in ALPGEN to the factorisation and normalisation scale and to the matching parameters, and yield a 24% uncertainty on the cross-section ratio. Background contributions from the heavy-quark processes Wb ¯b, Wc¯c and Wc have relative uncer-tainties of 50%, estimated using a tag-counting method in con-trol regions. The t¯t cross-section is normalised to the approx-imate NNLO-predicted value obtained using HATHOR [51]. The SM single top-quark production cross-section is also calcu-lated to approximate NNLO [52–54]. A theoretical uncertainty of 10% is assigned for SM top-quark production. The normal-isation of the cross-section for production of diboson events is obtained using NLO cross-section predictions and has an un-certainty of 5%.
Multijet events may be selected if a jet is misidentified as an isolated lepton or if the event has a non-prompt lepton that ap-pears isolated. A binned maximum-likelihood fit to the Emiss
T
distribution is used to estimate the multijet background normal-isation. A template of the multijet background is modelled us-ing electron-like jets selected from jet-triggered collision data and is referred to as a jet-electron model. Each jet has to ful-fil the same pT and η requirements as a signal lepton, contain
at least four tracks to reduce the contribution from converted photons, and deposit 80–95% of its energy in the electromag-netic calorimeter. The uncertainty in the multijet background normalisation is estimated to be 50% by fitting the distribution of mWT instead of ETmiss, and using jet-electron models built from jet-triggered data samples with different average numbers of in-elastic pp interactions per event. The shape of the jet-electron data sample is used to model the multijet background shape in the electron and muon channels. The validity of the model in both channels is verified by comparing distributions of multijet-sensitive variables to observed data.
In the b-tagged sample 26223 events are observed in data compared to a prediction of 24000± 7000 events from our esti-mates of SM backgrounds. Table 1 summarises the event yield for each of the background processes considered. Each event yield uncertainty in Table 1 combines the statistical uncertainty, originating from the limited size of the used samples, with the uncertainty in the cross-section or normalisation.
4. Data analysis
Given the large uncertainty in the expected background and the small number of expected signal events estimated in Sec-tion 3, multivariate analysis techniques are used to separate sig-nal events from background events. We use a neural-network classifier [55] that combines a three-layer feed-forward neural network with a complex robust preprocessing. In order to im-prove the performance and to avoid overtraining, Bayesian reg-ularisation [56] is implemented during the training process. The
[GeV] W T p 0 20 40 60 80 100 120 140 160 180 200 Events/6.7 GeV 0 50 100 150 200 250 300 3 10 × data tt single top Z+jets W+jets Wbb,Wcc,Wc multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb ∫ ATLAS Pretagged R(jet,lep) ∆ 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Events/0.2 0 50 100 150 200 250 300 350×103 data t t single top Z+jets W+jets ,Wc c ,Wc b Wb multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb ∫ ATLAS Pretagged Lepton charge -2 -1 0 1 2 Events 0 200 400 600 800 1000 1200 1400 1600 1800 2000 3 10 × data tt single top Z+jets W+jets Wbb,Wcc,Wc multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb ∫ ATLAS Pretagged (a) (b) (c) [GeV] W T p 0 20 40 60 80 100 120 140 160 180 200 Events/6.7 GeV 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 data =100 pb) σ FCNC ( t t single top Z+jets W+jets ,Wc c ,Wc b Wb multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb ∫ ATLAS b-tagged R(b-jet,lep) ∆ 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Events/0.2 0 1000 2000 3000 4000 5000 6000 7000 data =100 pb) σ FCNC ( t t single top Z+jets W+jets ,Wc c ,Wc b Wb multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb ∫ ATLAS b-tagged Lepton charge -2 -1 0 1 2 Events 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 data FCNC (σ=100 pb) t t single top Z+jets W+jets ,Wc c ,Wc b Wb multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb ∫ ATLAS b-tagged (d) (e) (f)
Figure 1: Kinematic distributions of the three most significant variables normalised to the number of observed events for the pretagged selection (top) and in the
b-tagged selection (bottom), for the electron and muon channel combined: (a), (d) transverse momentum of the W boson, (b), (e) ∆R between the jet and the lepton
and (c), (f) charge of the lepton. In these distributions the signal contribution is shown stacked on top of the backgrounds, with a normalisation corresponding to a cross-section of 100 pb. The hatched band indicates the statistical uncertainty from the sizes of the simulated samples and the uncertainty in the background normalisation.
Table 1: Number of observed data events and expected number of background events for the b-tagged sample. The uncertainties include the statistical un-certainty from the size of the simulated sample and the uncertainties on the cross-section and the multijet normalisation.
Process Expected events
SM single top 1460 ± 150 t¯t 660 ± 70 W+light jets 4700 ± 1100 Wb ¯b/Wc¯c+jets 2700 ± 1500 Wc + jets 12100 ± 6700 Z+jets/diboson 700 ± 170 Multijet 1600 ± 800 Total background 24000 ± 7000 Observed 26223
network infrastructure consists of one input node for each of the 11 input variables plus one bias node, 13 nodes in the hidden layer, and one output node which gives a continuous output in the interval [−1, 1]. The training is done with a mixture of 50% signal and 50% background events using about 650000 events, where the different background processes are weighted accord-ing to their expected numbers of events.
The qg→ t → bℓν process is characterised by three main dif-ferences from SM processes that pass the event selection cuts. Firstly, in single top-quark production via FCNCs, the top quark is produced almost without transverse momentum. Therefore
the pT distribution of the top quark is much softer than the
pT distribution of top quarks produced through SM top-quark
production, and the W boson and b quark from the top-quark decay are almost back-to-back with an opening angle near π. Secondly, unlike in the W/Z+jet and diboson backgrounds, the W boson from the top-quark decay has a very high momentum and its highly-boosted decay products have small opening an-gles. Lastly, the top-quark charge asymmetry differs between FCNC processes and SM processes. The FCNC processes are predicted to produce four times more single top quarks than anti-top quarks, whereas in SM single top-quark production and all other SM backgrounds this ratio is at most two. All possi-ble discriminating variapossi-bles such as momenta, relative angles, pseudorapidity, reconstructed particles masses, and lepton elec-tric charge were explored, including variables obtained from the reconstructed W boson and the top quark. To reconstruct the four-momentum of the W boson, the neutrino four-momentum is derived from the measured ~Emiss
T since it cannot be measured
directly. The neutrino longitudinal momentum, pνz, is calculated by imposing a kinematic constraint on the mW invariant mass.
The twofold ambiguity is resolved by choosing the smallest|pνz| solution, since the W boson is expected to be produced with small pseudorapidity. The top-quark candidate is reconstructed by adding the momentum of the b-tagged jet to the four-momentum of the reconstructed W boson.
Eleven variables were selected as input to the neural network after testing for each variable the agreement between the
back-NN output -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Events/0.07 0 50 100 150 200 250 3 10 × data tt
single top Z+jets
W+jets Wbb,Wcc,Wc multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb
∫
ATLAS Pretagged NN output -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Events/0.07 0 500 1000 1500 2000 2500 3000 3500 4000 data =100 pb) σ FCNC ( t t single top Z+jets W+jets ,Wc c ,Wc b Wb multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb∫
ATLAS b-tagged (a) (b)Figure 2: (a) Neural network output distribution scaled to the number of observed events in the pretagged sample. (b) Neural network output distribution scaled to the number of observed events in the b-tagged sample. In these distributions the signal contribution is shown stacked on top of the backgrounds. The hatched band indicates the statistical uncertainty from the sizes of the simulated samples and the uncertainty in the background normalisation.
ground model and observed events in both the large sample of pretagged events and the b-tagged sample. The first ten vari-ables are the charge and the pT of the lepton, the pT, η and
mass of the b-tagged jet, the ∆R between the b-tagged jet and the charged lepton, the ∆R between the b-tagged jet and the reconstructed W boson, the opening angle ∆φ between the di-rections of the b-tagged jet and the reconstructed W boson, the pT of the W boson and the reconstructed top-quark mass. The
last variable considered in the neural network is the W-boson helicity. This is calculated as cos θ∗, the cosine of the angle
between the momentum of the charged lepton in the W-boson rest-frame and the momentum of the W boson as seen in the top-quark rest-frame. Table 2 shows a summary of the used ables ordered by their importance. The importance of the vari-ables is estimated using an iterative procedure, removing one variable at a time and recalculating the separation power. The ordering is done in terms of relevance defined as standard de-viations of the additional separation power given by each vari-able. Distributions of the three most important variables in the pretagged sample and the b-tagged sample, normalised to the number of observed events, are shown in Figure 1. Since the neural network benefits from the correlation between variables and is trained to separate the signal process from all background processes, the naively expected variables are not the most im-portant ones, but variables, which are highly correlated to them.
The resulting neural network output distributions for the var-ious processes, scaled to the number of observed events in the pretagged sample are shown in Figure 2(a). Figure 2(b) shows these distributions in the b-tagged sample. Signal-like events have output values close to 1, whereas background-like events are accumulated near −1. We find good agreement between the neural network output distributions for data and simulated events in both the pretagged and b-tagged samples.
Table 2: Variables used as input to the neural network ordered by their impor-tance. Variable Significance (σ) pW T 57 ∆R(b-jet,lep) 28 Lepton charge 22 mtop 20 mb-jet 15 ηb-jet 12 ∆φ(W,b-jet) 11 plepT 12 pbT-jet 6.5 cos θ∗ 5.7 ∆R(W,b-jet) 5.0 5. Systematic Uncertainties
Systematic uncertainties affect the signal acceptance, the nor-malisation of the individual backgrounds, and the shape of the neural network output distributions. All uncertainties described below lead to uncertainties in the rate estimation as well as dis-tortions of the neural network output distribution and are imple-mented as such in the statistical analysis.
The momentum scale and resolution, as well as the trigger and identification efficiency for single leptons is measured in collision data using Z → ee, Z → µµ, and W → eν de-cays and corrective scale factors are applied to the simulation. Uncertainties on these factors as functions of the lepton kine-matics are around 5%. To evaluate the effect of momentum scale uncertainties, the event selection is repeated with the lep-ton momentum varied up and down by the uncertainty. For the momentum resolution uncertainties, the event selection is
re-peated with the lepton momentum smeared. The uncertainty in the jet energy scale, derived using information from test-beam data, collision data, and simulation varies between 2.5% and 8% (3.5% and 14%) in the central (forward) region, depending on jet pTand η [46]. This includes uncertainties due to
differ-ent compositions of jets initiated by gluons or light quarks in the samples and mis-measurements due to close-by jets. Addi-tional uncertainties due to multiple pp interactions are as large as 5% (7%) in the central (forward) region. Here, the central region is defined as|η| < 0.8. An additional jet energy scale uncertainty of up to 2.5%, depending on the pT of the jet, is
applied for b-quark jets due to differences between jets initi-ated by gluons or light quarks as opposed to jets containing b-hadrons. To evaluate the effect of these uncertainties the en-ergy of each jet is scaled up or down by the uncertainty and the change is also propagated to the missing transverse momen-tum calculation. An uncertainty of 2% is assigned for the jet reconstruction efficiency based on the agreement between effi-ciencies measured in minimum bias and QCD dijet events and simulated events [57]. For the b-tagging efficiencies and mis-tag rates, jet pT- and η-dependent scale factors are applied to
match simulated distributions with observed distributions and have uncertainties from 8–16% and 23–45%, respectively [48]. Systematic effects from mis-modelling in event generators are estimated by comparing different generators and varying parameters for the event generation. The effect of parton shower and hadronisation modelling uncertainties is evaluated by comparing two AcerMC samples interfaced to HERWIG and PYTHIA, respectively. The amount of initial and final state radiation is varied by modifying parameters in PYTHIA. The parameters are varied in a range comparable to those used in the Perugia Soft/Hard tune variations [58]. These uncertainties, the parton shower modelling and variations of initial and final state radiation are evaluated for all processes involving top quarks including the signal. The impact of the choice of PDFs in the simulation is studied by re-weighting the events according to PDF uncertainty eigenvector sets (CTEQ6.6, MSTW2008 [59]) and estimated following the procedure described in [60]. The uncertainties for the two PDF sets are added in quadrature. To account for uncertainties connected with the simulation of the W+jets sample several parameters in the generation of these samples are varied and event kinematics are compared. The un-certainty in the measured integrated luminosity is estimated to be 3.7%.
The dominant uncertainties are the uncertainties in the jet energy scale, the initial and final state radiation variations, and uncertainties in the b-tagging efficiencies and mis-tag rates. 6. Results
A Bayesian statistical analysis [61, 62] using a binned likeli-hood method applied to the neural network output distributions for the electron and muon channel combined is performed to measure or set an upper limit on the FCNC single top-quark production cross-section.
Systematic uncertainties and their correlations among pro-cesses are included with a direct sampling approach where the
[pb] σ 0 2 4 6 8 10 12 14 Posterior Density 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ATLAS
∫
L dt = 2.05 fb-1 = 7 TeV s Expected limit < 2.4 pb @ 95% C.L. σ (a) [pb] σ 0 2 4 6 8 10 12 14 Posterior Density 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 ATLAS∫
L dt = 2.05 fb-1 = 7 TeV s Observed limit < 3.9 pb @ 95% C.L. σ (b)Figure 3: Distribution of the posterior probability function including all sys-tematic uncertainties for (a) the expected upper limit and (b) the observed upper limit at 95% C.L..
same Gaussian shift is applied to each source, process, and bin for a given uncertainty. The posterior density function (pdf) is obtained by creating a large number of samples of systematic shifts. A separate likelihood distribution is obtained for each sample, and the final pdf is then the average over all of the in-dividual likelihoods. This pdf gives the probability of the sig-nal hypothesis as a function of the sigsig-nal cross-section. Since no significant rate of FCNC single top-quark production is ob-served, an upper limit is set by integrating the pdf. To estimate the a priori sensitivity, we use a pseudo-dataset corresponding to the prediction from simulations (Asimov dataset) [63] and treated in the same way as the observed dataset. The result-ing expected upper limit at 95% confidence level (C.L.) on the anomalous FCNC single top-quark production cross-section in-cluding all systematic uncertainties is 2.4 pb, while the cor-responding observed upper limit is 3.9 pb, as shown in Fig-ures 3(a) and 3(b), respectively. To visualise the observed upper limit in the neural network output distribution Figure 4 shows
the FCNC single top-quark process scaled to observed upper limit on top of the SM background processes. As a cross-check we performed the full statistical analysis only for events with NN output > 0, which yields an observed upper limit at 95% C.L. of 5.9 pb. Using the NLO predictions for the FCNC sin-gle top-quark production cross-section [64, 65], the measured upper limit on the production cross-section is converted into limits on the coupling constants κugt/Λ and κcgt/Λ. Assuming κcgt/Λ = 0 one finds κugt/Λ < 6.9 · 10−3 TeV−1 and
assum-ing κugt/Λ = 0 one finds κcgt/Λ < 1.6 · 10−2 TeV−1.
Fig-ure 5(a) shows the distribution of the upper limit for all pos-sible combinations. Using the NLO calculation [66], upper limits on the branching fractionsB(t → ug) < 5.7 · 10−5 as-sumingB(t → cg) = 0, and B(t → cg) < 2.7 · 10−4assuming B(t → ug) = 0 are derived, as shown in Figure 5(b).
NN output 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 Events/0.07 0 100 200 300 400 500 600 700 data = 3.9 pb) σ FCNC ( t t single top Z+jets W+jets ,Wc c ,Wc b Wb multijet uncertainty = 7 TeV s , -1 L dt = 2.05 fb
∫
ATLAS b-taggedFigure 4: Distributions of the neural network output: Observed signal and simulated background output distribution normalised to the mean value of the marginalised nuisance parameters, zoomed into the signal region. The FCNC single top quark process is normalised to the observed limit of 3.9 pb. The hatched band indicates the statistical uncertainty from the sizes of the simu-lated samples and the uncertainty in the background normalisation.
7. Conclusion
In summary, a data sample selected to consist of events with an isolated electron or muon, missing transverse momentum and a b-quark jet has been used to search for FCNC produc-tion of single top-quarks at the LHC. No evidence for such processes is found and the upper limit at 95% C.L. on the production cross-section is 3.9 pb. The limits set on the cou-pling constants κugt/Λ and κcgt/Λ and the branching fractions B(t → ug) < 5.7 · 10−5 assuming B(t → cg) = 0, and
B(t → cg) < 2.7 · 10−4 assumingB(t → ug) = 0 are the
most stringent to date on FCNC single top-quark production processes for qg → t and improve on the previous best lim-its [25] by factors of 4 and 15, respectively.
] -1 [TeV Λugt κ 0 1 2 3 4 5 6 7 -3 10 × ] -1 [ T e V Λ cgt κ 0 2 4 6 8 10 12 14 16 18 20 -3 10 × Observed Expected ATLAS Excluded region -1 L dt = 2.05 fb
∫
= 7 TeV s (a) ug) → B(t 0.00 0.01 0.02 0.03 0.04 0.05 0.06 -3 10 × cg) → B(t 0.00 0.05 0.10 0.15 0.20 0.25 0.30 -3 10 × Observed Expected ATLAS Excluded region -1 L dt = 2.05 fb∫
= 7 TeV s (b)Figure 5: Upper limit (a) on the coupling constants κugt/Λ and κcgt/Λ and (b)
on the branching fractions t→ ug and t → cg.
8. Acknowledgements
We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently.
We acknowledge the support of ANPCyT, Argentina; Yer-PhI, Armenia; ARC, Australia; BMWF, Austria; ANAS, Azer-baijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; EPLANET and ERC, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNAS, Georgia; BMBF, DFG, HGF, MPG and AvH Founda-tion, Germany; GSRT, Greece; ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Nor-way; MNiSW, Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Russia and ROSATOM, Russian
Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of Amer-ica.
The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Den-mark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide.
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D. Cˆot´e29, R. Coura Torres23a, L. Courneyea168, G. Cowan75, C. Cowden27, B.E. Cox81, K. Cranmer107, F. Crescioli121a,121b,
M. Cristinziani20, G. Crosetti36a,36b, R. Crupi71a,71b, S. Cr´ep´e-Renaudin55, C.-M. Cuciuc25a, C. Cuenca Almenar174, T. Cuhadar Donszelmann138, M. Curatolo47, C.J. Curtis17, C. Cuthbert149, P. Cwetanski60, H. Czirr140, P. Czodrowski43, Z. Czyczula174, S. D’Auria53, M. D’Onofrio72, A. D’Orazio131a,131b, P.V.M. Da Silva23a, C. Da Via81, W. Dabrowski37, T. Dai86, C. Dallapiccola83, M. Dam35, M. Dameri50a,50b, D.S. Damiani136, H.O. Danielsson29, D. Dannheim98, V. Dao49, G. Darbo50a,
G.L. Darlea25b, W. Davey20, T. Davidek125, N. Davidson85, R. Davidson70, E. Davies117,c, M. Davies92, A.R. Davison76,
Y. Davygora58a, E. Dawe141, I. Dawson138, J.W. Dawson5,∗, R.K. Daya-Ishmukhametova22, K. De7, R. de Asmundis101a,
S. De Castro19a,19b, P.E. De Castro Faria Salgado24, S. De Cecco77, J. de Graat97, N. De Groot103, P. de Jong104, C. De La Taille114,
H. De la Torre79, B. De Lotto163a,163c, L. de Mora70, L. De Nooij104, D. De Pedis131a, A. De Salvo131a, U. De Sanctis163a,163c,
A. De Santo148, J.B. De Vivie De Regie114, S. Dean76, W.J. Dearnaley70, R. Debbe24, C. Debenedetti45, D.V. Dedovich64,
J. Degenhardt119, M. Dehchar117, C. Del Papa163a,163c, J. Del Peso79, T. Del Prete121a,121b, T. Delemontex55, M. Deliyergiyev73,
A. Dell’Acqua29, L. Dell’Asta21, M. Della Pietra101a,i, D. della Volpe101a,101b, M. Delmastro4, N. Delruelle29, P.A. Delsart55,
C. Deluca147, S. Demers174, M. Demichev64, B. Demirkoz11,k, J. Deng162, S.P. Denisov127, D. Derendarz38, J.E. Derkaoui134d,
F. Derue77, P. Dervan72, K. Desch20, E. Devetak147, P.O. Deviveiros104, A. Dewhurst128, B. DeWilde147, S. Dhaliwal157,
R. Dhullipudi24,l, A. Di Ciaccio132a,132b, L. Di Ciaccio4, A. Di Girolamo29, B. Di Girolamo29, S. Di Luise133a,133b, A. Di Mattia171,
B. Di Micco29, R. Di Nardo47, A. Di Simone132a,132b, R. Di Sipio19a,19b, M.A. Diaz31a, F. Diblen18c, E.B. Diehl86, J. Dietrich41, T.A. Dietzsch58a, S. Diglio85, K. Dindar Yagci39, J. Dingfelder20, C. Dionisi131a,131b, P. Dita25a, S. Dita25a, F. Dittus29, F. Djama82, T. Djobava51b, M.A.B. do Vale23c, A. Do Valle Wemans123a, T.K.O. Doan4, M. Dobbs84, R. Dobinson29,∗, D. Dobos29,
E. Dobson29,m, J. Dodd34, C. Doglioni49, T. Doherty53, Y. Doi65,∗, J. Dolejsi125, I. Dolenc73, Z. Dolezal125, B.A. Dolgoshein95,∗, T. Dohmae154, M. Donadelli23d, M. Donega119, J. Donini33, J. Dopke29, A. Doria101a, A. Dos Anjos171, M. Dosil11,
A. Dotti121a,121b, M.T. Dova69, J.D. Dowell17, A.D. Doxiadis104, A.T. Doyle53, Z. Drasal125, J. Drees173, N. Dressnandt119, H. Drevermann29, C. Driouichi35, M. Dris9, J. Dubbert98, S. Dube14, E. Duchovni170, G. Duckeck97, A. Dudarev29, F. Dudziak63,
M. D¨uhrssen29, I.P. Duerdoth81, L. Duflot114, M-A. Dufour84, M. Dunford29, H. Duran Yildiz3a, R. Duxfield138, M. Dwuznik37,
F. Dydak29, M. D¨uren52, W.L. Ebenstein44, J. Ebke97, S. Eckweiler80, K. Edmonds80, C.A. Edwards75, N.C. Edwards53,
W. Ehrenfeld41, T. Ehrich98, T. Eifert142, G. Eigen13, K. Einsweiler14, E. Eisenhandler74, T. Ekelof165, M. El Kacimi134c,
M. Ellert165, S. Elles4, F. Ellinghaus80, K. Ellis74, N. Ellis29, J. Elmsheuser97, M. Elsing29, D. Emeliyanov128, R. Engelmann147,
A. Engl97, B. Epp61, A. Eppig86, J. Erdmann54, A. Ereditato16, D. Eriksson145a, J. Ernst1, M. Ernst24, J. Ernwein135, D. Errede164,
S. Errede164, E. Ertel80, M. Escalier114, C. Escobar122, X. Espinal Curull11, B. Esposito47, F. Etienne82, A.I. Etienvre135,
E. Etzion152, D. Evangelakou54, H. Evans60, L. Fabbri19a,19b, C. Fabre29, R.M. Fakhrutdinov127, S. Falciano131a, Y. Fang171,
M. Fanti88a,88b, A. Farbin7, A. Farilla133a, J. Farley147, T. Farooque157, S.M. Farrington117, P. Farthouat29, P. Fassnacht29,
D. Fassouliotis8, B. Fatholahzadeh157, A. Favareto88a,88b, L. Fayard114, S. Fazio36a,36b, R. Febbraro33, P. Federic143a, O.L. Fedin120,
W. Fedorko87, M. Fehling-Kaschek48, L. Feligioni82, D. Fellmann5, C. Feng32d, E.J. Feng30, A.B. Fenyuk127, J. Ferencei143b, J. Ferland92, W. Fernando108, S. Ferrag53, J. Ferrando53, V. Ferrara41, A. Ferrari165, P. Ferrari104, R. Ferrari118a,
D.E. Ferreira de Lima53, A. Ferrer166, M.L. Ferrer47, D. Ferrere49, C. Ferretti86, A. Ferretto Parodi50a,50b, M. Fiascaris30, F. Fiedler80, A. Filipˇciˇc73, A. Filippas9, F. Filthaut103, M. Fincke-Keeler168, M.C.N. Fiolhais123a,h, L. Fiorini166, A. Firan39, G. Fischer41, P. Fischer20, M.J. Fisher108, M. Flechl48, I. Fleck140, J. Fleckner80, P. Fleischmann172, S. Fleischmann173, T. Flick173, A. Floderus78, L.R. Flores Castillo171, M.J. Flowerdew98, M. Fokitis9, T. Fonseca Martin16, D.A. Forbush137, A. Formica135, A. Forti81, D. Fortin158a, J.M. Foster81, D. Fournier114, A. Foussat29, A.J. Fowler44, K. Fowler136, H. Fox70, P. Francavilla11,
S. Franchino118a,118b, D. Francis29, T. Frank170, M. Franklin57, S. Franz29, M. Fraternali118a,118b, S. Fratina119, S.T. French27,
F. Friedrich43, R. Froeschl29, D. Froidevaux29, J.A. Frost27, C. Fukunaga155, E. Fullana Torregrosa29, J. Fuster166, C. Gabaldon29,
O. Gabizon170, T. Gadfort24, S. Gadomski49, G. Gagliardi50a,50b, P. Gagnon60, C. Galea97, E.J. Gallas117, V. Gallo16, B.J. Gallop128,
P. Gallus124, K.K. Gan108, Y.S. Gao142,e, V.A. Gapienko127, A. Gaponenko14, F. Garberson174, M. Garcia-Sciveres14, C. Garc´ıa166,
J.E. Garc´ıa Navarro166, R.W. Gardner30, N. Garelli29, H. Garitaonandia104, V. Garonne29, J. Garvey17, C. Gatti47, G. Gaudio118a,
D.A.A. Geerts104, Ch. Geich-Gimbel20, K. Gellerstedt145a,145b, C. Gemme50a, A. Gemmell53, M.H. Genest55, S. Gentile131a,131b, M. George54, S. George75, P. Gerlach173, A. Gershon152, C. Geweniger58a, H. Ghazlane134b, N. Ghodbane33, B. Giacobbe19a, S. Giagu131a,131b, V. Giakoumopoulou8, V. Giangiobbe11, F. Gianotti29, B. Gibbard24, A. Gibson157, S.M. Gibson29,
L.M. Gilbert117, V. Gilewsky90, D. Gillberg28, A.R. Gillman128, D.M. Gingrich2,d, J. Ginzburg152, N. Giokaris8, M.P. Giordani163c, R. Giordano101a,101b, F.M. Giorgi15, P. Giovannini98, P.F. Giraud135, D. Giugni88a, M. Giunta92, P. Giusti19a, B.K. Gjelsten116,
L.K. Gladilin96, C. Glasman79, J. Glatzer48, A. Glazov41, K.W. Glitza173, G.L. Glonti64, J.R. Goddard74, J. Godfrey141,
J. Godlewski29, M. Goebel41, T. G¨opfert43, C. Goeringer80, C. G¨ossling42, T. G¨ottfert98, S. Goldfarb86, T. Golling174,
A. Gomes123a,b, L.S. Gomez Fajardo41, R. Gonc¸alo75, J. Goncalves Pinto Firmino Da Costa41, L. Gonella20, A. Gonidec29,
S. Gonzalez171, S. Gonz´alez de la Hoz166, G. Gonzalez Parra11, M.L. Gonzalez Silva26, S. Gonzalez-Sevilla49, J.J. Goodson147,
L. Goossens29, P.A. Gorbounov94, H.A. Gordon24, I. Gorelov102, G. Gorfine173, B. Gorini29, E. Gorini71a,71b, A. Goriˇsek73,
E. Gornicki38, S.A. Gorokhov127, V.N. Goryachev127, B. Gosdzik41, M. Gosselink104, M.I. Gostkin64, I. Gough Eschrich162,
M. Gouighri134a, D. Goujdami134c, M.P. Goulette49, A.G. Goussiou137, C. Goy4, S. Gozpinar22, I. Grabowska-Bold37,
P. Grafstr¨om29, K-J. Grahn41, F. Grancagnolo71a, S. Grancagnolo15, V. Grassi147, V. Gratchev120, N. Grau34, H.M. Gray29,
J.A. Gray147, E. Graziani133a, O.G. Grebenyuk120, T. Greenshaw72, Z.D. Greenwood24,l, K. Gregersen35, I.M. Gregor41,
P. Grenier142, J. Griffiths137, N. Grigalashvili64, A.A. Grillo136, S. Grinstein11, Y.V. Grishkevich96, J.-F. Grivaz114, M. Groh98, E. Gross170, J. Grosse-Knetter54, J. Groth-Jensen170, K. Grybel140, V.J. Guarino5, D. Guest174, C. Guicheney33, A. Guida71a,71b, S. Guindon54, H. Guler84,n, J. Gunther124, B. Guo157, J. Guo34, A. Gupta30, Y. Gusakov64, V.N. Gushchin127, P. Gutierrez110, N. Guttman152, O. Gutzwiller171, C. Guyot135, C. Gwenlan117, C.B. Gwilliam72, A. Haas142, S. Haas29, C. Haber14,
H.K. Hadavand39, D.R. Hadley17, P. Haefner98, F. Hahn29, S. Haider29, Z. Hajduk38, H. Hakobyan175, D. Hall117, J. Haller54,
K. Hamacher173, P. Hamal112, M. Hamer54, A. Hamilton144b,o, S. Hamilton160, H. Han32a, L. Han32b, K. Hanagaki115,
K. Hanawa159, M. Hance14, C. Handel80, P. Hanke58a, J.R. Hansen35, J.B. Hansen35, J.D. Hansen35, P.H. Hansen35, P. Hansson142,
K. Hara159, G.A. Hare136, T. Harenberg173, S. Harkusha89, D. Harper86, R.D. Harrington45, O.M. Harris137, K. Harrison17,
J. Hartert48, F. Hartjes104, T. Haruyama65, A. Harvey56, S. Hasegawa100, Y. Hasegawa139, S. Hassani135, M. Hatch29, D. Hauff98,
S. Haug16, M. Hauschild29, R. Hauser87, M. Havranek20, B.M. Hawes117, C.M. Hawkes17, R.J. Hawkings29, A.D. Hawkins78,
D. Hawkins162, T. Hayakawa66, T. Hayashi159, D. Hayden75, H.S. Hayward72, S.J. Haywood128, E. Hazen21, M. He32d, S.J. Head17,
V. Hedberg78, L. Heelan7, S. Heim87, B. Heinemann14, S. Heisterkamp35, L. Helary4, C. Heller97, M. Heller29, S. Hellman145a,145b,
D. Hellmich20, C. Helsens11, R.C.W. Henderson70, M. Henke58a, A. Henrichs54, A.M. Henriques Correia29, S. Henrot-Versille114,
F. Henry-Couannier82, C. Hensel54, T. Henß173, C.M. Hernandez7, Y. Hern´andez Jim´enez166, R. Herrberg15, A.D. Hershenhorn151,
G. Herten48, R. Hertenberger97, L. Hervas29, G.G. Hesketh76, N.P. Hessey104, E. Hig ´on-Rodriguez166, D. Hill5,∗, J.C. Hill27, N. Hill5, K.H. Hiller41, S. Hillert20, S.J. Hillier17, I. Hinchliffe14, E. Hines119, M. Hirose115, F. Hirsch42, D. Hirschbuehl173, J. Hobbs147, N. Hod152, M.C. Hodgkinson138, P. Hodgson138, A. Hoecker29, M.R. Hoeferkamp102, J. Hoffman39, D. Hoffmann82, M. Hohlfeld80, M. Holder140, S.O. Holmgren145a, T. Holy126, J.L. Holzbauer87, Y. Homma66, T.M. Hong119,
L. Hooft van Huysduynen107, T. Horazdovsky126, C. Horn142, S. Horner48, J-Y. Hostachy55, S. Hou150, M.A. Houlden72,
A. Hoummada134a, J. Howarth81, D.F. Howell117, I. Hristova15, J. Hrivnac114, I. Hruska124, T. Hryn’ova4, P.J. Hsu80, S.-C. Hsu14, G.S. Huang110, Z. Hubacek126, F. Hubaut82, F. Huegging20, A. Huettmann41, T.B. Huffman117, E.W. Hughes34, G. Hughes70,
R.E. Hughes-Jones81, M. Huhtinen29, P. Hurst57, M. Hurwitz14, U. Husemann41, N. Huseynov64,p, J. Huston87, J. Huth57,
G. Iacobucci49, G. Iakovidis9, M. Ibbotson81, I. Ibragimov140, R. Ichimiya66, L. Iconomidou-Fayard114, J. Idarraga114, P. Iengo101a,
O. Igonkina104, Y. Ikegami65, M. Ikeno65, Y. Ilchenko39, D. Iliadis153, N. Ilic157, M. Imori154, T. Ince20, J. Inigo-Golfin29,
P. Ioannou8, M. Iodice133a, V. Ippolito131a,131b, A. Irles Quiles166, C. Isaksson165, A. Ishikawa66, M. Ishino67, R. Ishmukhametov39,
C. Issever117, S. Istin18a, A.V. Ivashin127, W. Iwanski38, H. Iwasaki65, J.M. Izen40, V. Izzo101a, B. Jackson119, J.N. Jackson72,
P. Jackson142, M.R. Jaekel29, V. Jain60, K. Jakobs48, S. Jakobsen35, J. Jakubek126, D.K. Jana110, E. Jankowski157, E. Jansen76,
H. Jansen29, A. Jantsch98, M. Janus20, G. Jarlskog78, L. Jeanty57, K. Jelen37, I. Jen-La Plante30, P. Jenni29, A. Jeremie4, P. Jeˇz35,
S. J´ez´equel4, M.K. Jha19a, H. Ji171, W. Ji80, J. Jia147, Y. Jiang32b, M. Jimenez Belenguer41, G. Jin32b, S. Jin32a, O. Jinnouchi156,
M.D. Joergensen35, D. Joffe39, L.G. Johansen13, M. Johansen145a,145b, K.E. Johansson145a, P. Johansson138, S. Johnert41,
K.A. Johns6, K. Jon-And145a,145b, G. Jones117, R.W.L. Jones70, T.W. Jones76, T.J. Jones72, O. Jonsson29, C. Joram29, P.M. Jorge123a, J. Joseph14, J. Jovicevic146, T. Jovin12b, X. Ju171, C.A. Jung42, R.M. Jungst29, V. Juranek124, P. Jussel61, A. Juste Rozas11,
V.V. Kabachenko127, S. Kabana16, M. Kaci166, A. Kaczmarska38, P. Kadlecik35, M. Kado114, H. Kagan108, M. Kagan57, S. Kaiser98, E. Kajomovitz151, S. Kalinin173, L.V. Kalinovskaya64, S. Kama39, N. Kanaya154, M. Kaneda29, S. Kaneti27,
T. Kanno156, V.A. Kantserov95, J. Kanzaki65, B. Kaplan174, A. Kapliy30, J. Kaplon29, D. Kar43, M. Karagounis20, M. Karagoz117, M. Karnevskiy41, K. Karr5, V. Kartvelishvili70, A.N. Karyukhin127, L. Kashif171, G. Kasieczka58b, R.D. Kass108, A. Kastanas13, M. Kataoka4, Y. Kataoka154, E. Katsoufis9, J. Katzy41, V. Kaushik6, K. Kawagoe66, T. Kawamoto154, G. Kawamura80,
M.S. Kayl104, V.A. Kazanin106, M.Y. Kazarinov64, R. Keeler168, R. Kehoe39, M. Keil54, G.D. Kekelidze64, J. Kennedy97,
C.J. Kenney142, M. Kenyon53, O. Kepka124, N. Kerschen29, B.P. Kerˇsevan73, S. Kersten173, K. Kessoku154, J. Keung157,
F. Khalil-zada10, H. Khandanyan164, A. Khanov111, D. Kharchenko64, A. Khodinov95, A.G. Kholodenko127, A. Khomich58a,
T.J. Khoo27, G. Khoriauli20, A. Khoroshilov173, N. Khovanskiy64, V. Khovanskiy94, E. Khramov64, J. Khubua51b, H. Kim145a,145b,
M.S. Kim2, S.H. Kim159, N. Kimura169, O. Kind15, B.T. King72, M. King66, R.S.B. King117, J. Kirk128, L.E. Kirsch22,
